Systems and methods for predicting lower body poses

ABSTRACT

A computing system may receive sensor data from one or more sensors coupled to a user. Based on sensor data, the computing system may generate a first upper body pose that corresponds to a first portion of a body of the user, which comprises a head and an arm of the user. The computing system may process the upper body pose of the user to generate a temporal sequence of lower body poses comprising a first lower body pose associated with the first time and a second lower body pose associated with a second time, which may correspond to a second portion of the body of the user comprising a leg of the user. The computing system may generate one or more full body poses of the user based on at least the first upper body pose and the temporal sequence of lower body poses.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 17/024,591, filed 17 Sep. 2020, which isincorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to predicting lower body poses of auser.

BACKGROUND

Machine learning may be used to enable machines to automatically detectand process objects. In general, machine learning typically involvesprocessing a training data set in accordance with a machine-learningmodel and updating the model based on a training algorithm so that itprogressively “learns” the features in the data set that are predictiveof the desired outputs. One example of a machine-learning model is aneural network, which is a network of interconnected nodes. Groups ofnodes may be arranged in layers. The first layer of the network thattakes in input data may be referred to as the input layer, and the lastlayer that outputs data from the network may be referred to as theoutput layer. There may be any number of internal hidden layers that mapthe nodes in the input layer to the nodes in the output layer. In afeed-forward neural network, the outputs of the nodes in each layer—withthe exception of the output layer—are configured to feed forward intothe nodes in the subsequent layer.

Artificial reality is a form of reality that has been adjusted in somemanner before presentation to a user, which may include, e.g., a virtualreality (VR), an augmented reality (AR), a mixed reality (MR), a hybridreality, or some combination and/or derivatives thereof. Artificialreality content may include completely generated content or generatedcontent combined with captured content (e.g., real-world photographs).The artificial reality content may include video, audio, hapticfeedback, or some combination thereof, and any of which may be presentedin a single channel or in multiple channels (such as stereo video thatproduces a three-dimensional effect to the viewer). Artificial realitymay be associated with applications, products, accessories, services, orsome combination thereof, that are, e.g., used to create content in anartificial reality and/or used in (e.g., perform activities in) anartificial reality. The artificial reality system that provides theartificial reality content may be implemented on various platforms,including a head-mounted display (HMD) connected to a host computersystem, a standalone HMD, a mobile device or computing system, or anyother hardware platform capable of providing artificial reality contentto one or more viewers.

SUMMARY OF PARTICULAR EMBODIMENTS

Embodiments described herein relate to methods of generating one or morebody poses of a user associated with one more components of anartificial reality system. To enable a computing system to generate anaccurate pose of one or more joints that comprise a body pose, thecomputing system may receive one or more sensor data or image data fromone or more components of the artificial reality system, for examplesensor data from motion-tracking sensors or image data received from oneor more cameras. Using various techniques described herein, the imagedata and sensor data permits a computing system to accurately generatean upper body pose of a user based on the one or more sensor data orimage data associated with an artificial reality system. However, whilethis sensor data and image data is effective for generating upper bodyposes of a user, it is often of limited use when generating a lower bodyposes of the user.

To remedy this problem, particular embodiments described herein utilizea machine-learning model to generate a lower body pose in response toreceiving a generated upper body pose of a user. In particularembodiments, the machine-learning model may be trained to receive thegenerated upper body pose and generate a corresponding lower body pose.In particular embodiments, the machine-learning model may be based on aGenerative Adversarial Network (GAN) and trained using one or moretraining poses. Based on these training poses, the machine-learningmodel may learn how to produce realistic lower body poses. Once trained,the machine-learning model may receive generated upper body poses andoutput a corresponding lower body pose or full body pose that may beused in a variety of applications. For example, an outputted full bodypose can be used to generate an avatar of a user in a virtual reality orartificial reality space.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example artificial reality system.

FIG. 2 illustrates a sample body pose associated with user.

FIG. 3 illustrates an example of generating a lower body pose utilizinga known predicted upper body pose using a machine-learning model.

FIG. 4 illustrates a configuration for training a machine-learning modelfor lower body pose prediction.

FIG. 5 illustrates an example method for training a Generator.

FIG. 6 illustrates generating a full body pose from a generated upperbody pose and a generated lower body pose.

FIG. 7 illustrates an example method for training a Discriminator.

FIG. 8 illustrates an example method for generating a full body pose ofthe user based on an upper body pose and a lower body pose.

FIG. 9 illustrates an example network environment associated with asocial-networking system.

FIG. 10 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 1 illustrates an example artificial reality system 100. Inparticular embodiments, the artificial reality system 100 may comprise aheadset 104, a controller 106, and a computing system 108. A user 102may wear the headset 104 that may display visual artificial realitycontent to the user 102. The headset 104 may include an audio devicethat may provide audio artificial reality content to the user 102. Theheadset 104 may comprise one or more cameras 110 which can captureimages and videos of environments. The headset 104 may include an eyetracking system to determine a vergence distance of the user 102. Avergence distance may be a distance from the user's eyes to objects(e.g., real-world objects or virtual objects in a virtual space) thatthe user's eyes are converged at. The headset 104 may be referred to asa head-mounted display (HMD). In particular embodiments computing system108 may determine a pose of headset 104 associated with user 102. Theheadset pose may be determined by utilizing any of the sensor data orimage data received by the computing system 108.

One or more controllers 106 may be paired with the artificial realitysystem 100. In particular embodiments one or more controllers 106 may beequipped with at least one inertial measurement units (IMUs) andinfrared (IR) light emitting diodes (LEDs) for the artificial realitysystem 100 to estimate a pose of the controller and/or to track alocation of the controller, such that the user may perform certainfunctions via the controller. The one or more controllers 106 may beequipped with one or more trackable markers distributed to be tracked bythe computing system 108. The one or more controllers 106 may comprise atrackpad and one or more buttons. The one or more controllers 106 mayreceive inputs from the user 102 and relay the inputs to the computingsystem 108. The one or more controllers 106 may also provide hapticfeedback to the user 102. The computing system 108 may be connected tothe headset 104 and the one or more controllers 106 through cables orwireless connections. The one or more controllers 106 may include acombination of hardware, software, and/or firmware not explicitly shownherein so as not to obscure other aspects of the disclosure.

In particular embodiments the computing system 108 may receive sensordata from one or more sensors of artificial reality system 100. Inparticular embodiments the one or more sensors may be coupled to user102. In particular embodiments, the one or more sensors may beassociated with the headset 104 worn by the user. For example and not byway of limitation, the headset 104 may include a gyroscope or inertialmeasurement unit that tracks the user's real-time movements and outputsensor data to represent or describe the movement. The sensor dataprovided by such motion-tracking sensors may be used by the VRapplication to determine the user's current orientation and provide thatorientation to the rendering engine to orient/reorient the virtualcamera in the 3D space. As another example and not by way of limitation,the one or more controllers 106 may include inertial measurement units(IMUs) and infrared (IR) light emitting diodes (LEDs) configured tocollect and send IMU sensor data to the computing system 108. Inparticular embodiments the computing system 108 may utilize one or moresensor data with one or more tracking techniques, for example and not byway of limitation, SLAM tracking or IR-based tracking, to determine apose of one or more components of artificial reality system 100.

In particular embodiments the computing system 108 may receive one ormore image data from one or more components of artificial reality system100. In particular embodiments this image data comprises image datacaptured from one or more cameras 110 associated with artificial realitysystem 100. For example, FIG. 1 depicts one or more cameras 110 coupledwithin headset 104. These one or more cameras may be positioned tocapture one or more images associated with various perspectives, forexample and not by way of limitation, one or more cameras associatedwith headset 104 that face downward (e.g. towards the feet of user 102while standing).

In particular embodiments computing system 108 may determine acontroller pose of one or more controllers 106 associated with user 102.The controller pose associated with user 102 may be determined byutilizing sensor data or image data received by the computing system108, and utilizing one or more techniques, for example and not by way oflimitation, computer vision techniques (e.g., image classification).Methods for determining controller poses are described further in U.S.application Ser. No. 16/734,172, filed Jan. 3, 2020, entitled “JointInfrared and Visible Light Visual-Inertial Object Tracking,” herebyincorporated by reference in its entirety.

In particular embodiments the computing system 108 may control theheadset 104 and the one or more controllers 106 to provide theartificial reality content to and receive inputs from the user 102. Thecomputing system 108 may be a standalone host computer system, anon-board computer system integrated with the headset 104, a mobiledevice, or any other hardware platform capable of providing artificialreality content to and receiving inputs from the user 102.

FIG. 2 illustrates a sample full body pose associated with user 102. Inparticular embodiments, computing system 108 may generate a full bodypose 200 associated with user 102, comprising an upper body pose 205 anda lower body pose 215. Full body pose 200 associated with user 102 mayattempt to replicate a position and orientation of one or more joints ofa user 102 utilizing artificial reality system 100 at a particular time.In particular embodiments, full body pose 200 associated with user 102comprises a skeletal frame of inverse kinematics (“skeleton” or “bodypose”), which may comprise a list of one or more joints.

Upper body pose 205 may correspond to a portion of the body of the user102. In particular embodiments upper body pose 205 may correspond to aportion of the body of user 102 that comprises at least a head and armof the user 102. In particular embodiments upper body pose 205 may begenerated by determining a plurality of poses corresponding to aplurality of predetermined body parts or joints of the user 102, forexample, the head or wrist of the user 102. In particular embodimentsthe upper body pose 205 may further comprise, for example and not by wayof limitation, a pose of one or more joints associated with the upperbody of user 102, for example and not by way of limitation, a head pose210, a wrist pose 220, an elbow pose 220, a shoulder pose 240, a neckpose 250, or an upper spine pose 260.

Lower body pose 215 may correspond to a portion of the body of user 102.In particular embodiments lower body pose 215 may correspond to aportion of the body of user 102 that comprises at least a leg of theuser 102. In particular embodiments the lower body pose 215 may furthercomprise, for example and not by way of limitation, a joint pose of oneor more joints associated with the lower body of user 102, for exampleand not by way of limitation, a lower spine pose 270, a hip pose 280, aknee pose 290, or an ankle pose 295.

In particular embodiments the one or more joints poses comprising thefull body pose 200, the upper body pose 205, or the lower body pose 215may be represented through, for example and not by way of limitation, asubset of parameters that represent a position and/or orientation ofeach joint in a body pose 200. The non-linear solver may parametrizeeach joint pose associated with user 102 by 7 degrees of freedom: 3translation values (e.g., x, y, z), 3 rotation values (e.g., Eulerangles in radians), and 1 uniform scale value. In particular embodimentsthese parameters may be represented using one or more coordinatesystems, for example and not by way of limitation, via an absoluteglobal coordinate system (e.g., x, y, z) or via a localized coordinatesystem relative to a parent joint, for example and not by way oflimitation, a head joint.

In particular embodiments full body pose 200, upper body pose 205, orlower body pose 215, and one or more joint poses comprising the fullbody pose 200, upper body pose 205, or lower body pose 215, may bedetermined using received sensor or image data from one or morecomponents of artificial reality system 100. In particular embodimentsone or more joint poses comprising full body pose 200 may be generatedusing a combination of one or more techniques, for example and not byway of limitation localization techniques (e.g., SLAM), machine learningtechniques (e.g., a neural network), known spatial relationships withone or more artificial reality system components (e.g., a known spatialrelationship between headset 104 and head pose 210), visualizationtechniques (e.g., image segmentation), or optimization techniques (e.g.,a non-linear solver). In particular embodiments one or more of thesetechniques may be utilized separately or in conjunction with one or moreother techniques. This full body pose 200 associated with user 102 maybe useful for a variety of applications as described herein.

In particular embodiments, the computing system 108 may generate anupper body pose 205 of the user 102. The upper body pose 205 may begenerated based on at least sensor data or image data. In particularembodiments, computing system 108 may determine upper body pose 200utilizing one or more poses corresponding to a plurality ofpredetermined body parts of user 102, for example and not by way oflimitation, a joint pose of one or more joints comprising upper bodypose 205 (e.g., a head pose 210 or a wrist pose 220).

In particular embodiments the one or more poses may comprise a head pose210 associated with the user 102. The head pose 210 may comprise alocation and an orientation of the head joint of user 102 while wearingthe headset 104. The head pose 210 associated with user 102 may bedetermined by computing system 108 utilizing any of the sensor dataand/or image data received by the computing system 108. In particularembodiments this head pose 210 associated with user 102 may bedetermined based on the pose of the headset 104 and a known spatialrelationship between the headset 104 and the head of user 102. Inparticular embodiments the head pose 210 associated with user 102 may bedetermined based on sensor data associated with headset 104 worn by user102.

In particular embodiments the one or more poses may comprise a wristpose 220 associated with the user 102. The wrist pose 220 may comprise alocation and an orientation of a wrist joint of user 102 whileinteracting with artificial reality system 100. The wrist pose 220associated with user 102 may be determined by computing system 108utilizing any of the sensor data and/or image data received by thecomputing system 108. In particular embodiments the image data maycomprise one or more images captured by the headset 104 worn by the user102. These one or more images may depict a wrist of the user 102, or adevice held by user 102, (e.g., a controller 106). In particularembodiments the computing system 108 may use one or more computer visiontechniques, for example and not by way of limitation, imageclassification or object detection, to determine the wrist pose 220utilizing the one or more images. In particular embodiments the wristpose 220 may be determined based on the controller pose and a knownspatial relationship between the controller 106 and the wrist of user102.

In particular embodiments, upper body pose 205 may be inferred based onthe plurality of poses, for example and not by way of limitation, a headpose 210 or a wrist pose 220. In particular embodiments the upper bodypose 205 may be inferred by a non-linear kinematic optimization solver(“non-linear solver”) to infer one or more joint poses that compriseupper body pose 205. In particular embodiments the non-linear solver maycomprise a C++ library built for inverse kinematics with a large set ofcommon error functions that cover a wide range of applications. Inparticular embodiments the non-linear solver may provide one or morehelper functions for tasks that are usually related to global inversekinematics problems (e.g., joint and skeleton structures, meshes andlinear-blend skinning, error functions for common constraints) or one ormore helper functions for mesh deformations (e.g., Laplacian surfacedeformation) or one or more 10 functions for various file formats.

In particular embodiments the non-linear solver may infer one or morejoint poses comprising upper body pose 205. The one or more joint posescomprising upper body pose 205 associated with user 102 inferred by thenon-linear solver may comprise a kinematic hierarchy at a particulartime or state which is stored as a list of one or more joint poses. Inparticular embodiments the non-linear solver may include one or morebasic solvers supported to infer the pose of one or more jointscomprising upper body pose 205, for example and not by way of limitationa L-BFGS or a Gauss-Newton solver. In particular embodiments thenon-linear solver may utilize a skeletal solver function to solve for asingle frame of inverse kinematics (a single body pose). For example,the skeletal solver function may take a current one or more parameters(e.g., a joint pose) set as an input and optimize the activated subsetof parameters given the defined error functions. The convention of thenon-linear solver is to minimize the error value of the current function(e.g., the skeletal solver function) to infer an accurate upper bodypose 205.

In particular embodiments the non-linear solver may represent the one ormore joints poses through, for example and not by way of limitation, asubset of parameters that represent a position or orientation of eachjoint in an upper body pose 205. In particular embodiments thenon-linear solver may parametrize each joint pose by 7 degrees offreedom: 3 translation values (e.g., x, y, z), 3 rotation values (e.g.,Euler angles in radians), and 1 uniform scale value. In particularembodiments these parameters may be represented using one or morecoordinate systems, for example and not by way of limitation, via anabsolute global coordinate system (e.g., x, y, z) or via a localizedcoordinate system relative to a parent joint, for example and not by wayof limitation, a head joint.

In particular embodiments the non-linear solver may assign one or morevariable limits for each joint pose parameter (e.g., a minimum ormaximum value). In particular embodiments the non-linear solver mayassign predetermined static weights to each joint pose parameter. Thesepredetermined static weights may be determined, for example and not byway of limitation, based on the accuracy of the sensor data used todetermine the value of each variable. For example, the joint poseparameters representing the head pose 210 and wrist pose 220 associatedwith user 102 may be assigned a higher static weight because they aredetermined with more accurate methods, such as SLAM techniques asdescribed herein, than one or more other joint poses or variables withina joint pose parameter. In particular embodiments, the non-linear solvermay use the joint parameters and predetermined static weights to inferan upper body pose 205, which infers the most likely poses of one ormore joints of user 102 at a particular time or state.

While these techniques, utilized either alone or in combination,typically allow for reliable determination of the upper body pose 205and associated joints that comprise upper body pose 2015 (e.g., the headpose 210, wrist pose 220, elbow pose 230, etc.), these techniques may beunreliable and inaccurate for determining lower body pose 215 and theassociated joints that comprise lower body pose 215 (e.g., a hip pose280, a knee pose 290, an ankle pose 295, etc.). Due to user 102 onlywearing headset 104 and one or more controllers 106, limitations mayexist in the sensor or image data associated with artificial realitysystem 100. For example, image data captured from one or more cameras110 may not contain at least a portion of the lower body of user 102,resulting in little information with which to determine or generate anaccurate lower body pose 215 associated with user 102.

To remedy this problem, in particular embodiments computing system 108may utilize one or more techniques described herein to generate a lowerbody pose 215 of the user 102. In particular embodiments the lower bodypose 215 may be generated by processing the upper body pose 205 using amachine-learning model. FIG. 3 illustrates an example of generating alower body pose utilizing an inputted upper body pose. In particular,FIG. 3 illustrates an inputted upper body pose 205 associated with auser 102 of artificial reality system 100 generated using the methodsdescribed herein and received by machine-learning model 300. Inparticular embodiments, the machine-learning model 300 may be based on aGenerative Adversarial Network (GAN). Using particular embodimentsdescribed herein, machine-learning model 300 may utilize upper body pose205 to generate a lower body pose 215. In particular embodiments, thecomputing system may combine the generated upper body pose 205 with thegenerated lower body pose 215 to generate a full body pose in lieu of orin addition to the lower body pose 215 generated by the machine-learningmodel.

FIG. 4 illustrates a configuration for training a Generative AdversarialNetwork (GAN) 400 for pose prediction. GAN may include two separateneural networks, a Generator 405 (interchangeably referred to as “G”herein) and a Discriminator 410 (interchangeably referred to as “D”herein). In particular embodiments, the Generator 405 and theDiscriminator 410 may be implemented as, for example and not by way oflimitation, a neural network, although any network architecture suitablefor the operations described herein may be utilized. At a high level,the Generator 405 may be configured to receive as input a generatedupper body pose 205 and output a generated lower body pose 215. Inparticular embodiments the upper body pose 205 may be combined with thelower body pose 215 to generate a full body pose 425. In particularembodiments the Discriminator 410 may be configured to discriminatebetween “fake” full body poses 425 (those comprising lower body posesoutputted by the Generator 405) and “real” training lower body poses 435from a training pose database 440 that are not generated by Generator405. In particular embodiments, the one or more training full body poses435 may comprise full body poses from one or more images. The Generator405 and the Discriminator 410 may be considered as adversaries, becausethe objective of the Generator 405 is to generate fake poses that wouldfool the Discriminator 410 (in other words, to increase theDiscriminator's 410 error rate), and the objective of the Discriminator410 is to correctly distinguish “fake” poses from the Generator 405 and“real” poses. In particular embodiments, the machine-learning model(e.g., the Generator 405) is optimized during training to cause a secondmachine-learning model (e.g., the Discriminator 410) to incorrectlydetermine that a given full body pose, generated using themachine-learning model, is unlikely generated using the machine-learningmodel.

In particular embodiments, each training full body pose 435 in thetraining pose dataset may be “real” poses in the sense that they werenot generated, either in whole or in part, by the model 400. Inparticular embodiments, each training full body pose 435 may beautomatically obtained by retrieving different poses of a person from anpose database, wherein each of the different poses comprise a full bodypose 435 of the person. These “real” training full body poses 435 serveas ground truths to train the Discriminator 410 network to identifywhich full body poses are “real” and which are “fake”. The training fullbody pose 435 may also depict a person, for example and not by way oflimitation, sitting, standing, or kneeling, or in another posture thatis the same or differs from the generated upper body pose 205 or thegenerated lower body pose 215 outputted by the Generator 405. Therandomness in the poses helps the trained machine-learning model 400 tobe more robust, since in operation it may be unknown what kind of bodyposes the machine-learning model 400 would be asked to process.

In particular embodiments, training of the machine-learning model 200may be performed simultaneously, or in stages. For example, a firststage may be for training the Generator 405, a second stage may be fortraining the Discriminator 410 based on the outputs of the Generator405, and a third stage may be for retraining/refining the Generator 405to better “fool” the trained Discriminator 410. As another example, thetraining of Generator 405 and Discriminator 410 may occursimultaneously.

In particular embodiments, the Generator 405 may be configured toreceive an upper body pose 205 and generate a temporal sequence of lowerbody poses 215, for example, a sequence of lower body poses of a userstanding, sitting, or walking. Rather than generate a single lower bodypose 215, the Generator 405 may generate a temporal sequence of lowerbody poses that may be combined with a corresponding upper body pose togenerate a temporal sequence of full body poses. In particularembodiments the Discriminator 410 may be configured and trained todetermine whether an inputted sequence of poses is temporallyconsistent, and thus discriminate between a “fake” temporal sequence offull body poses (those comprising a temporal sequence of lower bodyposes outputted by the Generator 405) and a “real” temporal sequence oftraining lower body poses from a training pose database 440 that are notgenerated by Generator 405. In particular embodiments, the temporalsequence of training full body poses may comprise full body poses fromone or more images. In this temporal GAN the Generator 405 and theDiscriminator 410 may be considered as adversaries, because theobjective of the Generator 405 is to generate temporal sequences of fakeposes that would fool the Discriminator 410 (in other words, to increasethe Discriminator's 410 error rate), and the objective of theDiscriminator 410 is to correctly distinguish “fake” temporal sequencesof full body poses from the Generator 405 and “real” temporal sequencesof full body poses. Once trained, the temporal Generator can be trainedto output a realistic temporal sequence of lower body poses given aninputted upper body pose 205. While this disclosure, for readability andclarity, primarily describes training and utilizing only one pose at atime using the methods described herein, the GAN may instead be trainedand utilized to output a temporal sequence of lower body poses using thesame methods described herein.

FIG. 5 illustrates an example method 500 for training a Generator basedon a loss, in accordance with particular embodiments. At a high level,during the training the Generator 405, the parameters of the Generator405 may be iteratively updated based on a comparison between thegenerated “fake” full body pose 425 (which comprises lower body pose 215generated by the Generator) and the corresponding training full bodypose 435. An objective of training is to maximize the loss 460 ofprediction for “fake” poses. In doing so, the goal is to have theGenerator 405 learn how to generate, based on an upper body pose 205,upper body poses 215 that can be used to generate a “fake” full bodypose 425 that looks sufficiently “real.”

The method may begin at step 510, where a computing system may receivean upper body pose 205 that corresponds to a first portion of a body ofuser 102, the first portion of the body comprising a head and arm ofuser 102. The upper body pose 205 inputted into the Generator 405 may bean upper body pose generated based on sensor data and/or image datausing one or more techniques described herein, or the upper body pose205 inputted into the Generator 405 may be an upper body pose extractedfrom one or more real images of a person.

At step 520, the machine-learning model may generate, by processing theupper body pose 205, a lower body pose 215 that corresponds to a secondportion of the body of user 102, the second portion of the bodycomprising a leg of user 102. In particular embodiments the Generator405 may generate the lower body pose 215 based on the received upperbody pose 205. In particular embodiments the generated lower body pose215 corresponds to a second portion of the body comprising a leg of theuser. The generated lower body pose 215 may be used to generate a “fake”full body pose 425 that comprises the generated lower body pose 215.

In particular embodiments the computing system may determine contextualinformation associated with the artificial reality system 100. Inparticular embodiments, the Generator may receive the contextualinformation. The Generator 405 may utilize this contextual informationfor generating lower body poses. This contextual information may beassociated with a particular time at which the sensor data and/or imagedata was captured. For example and not by way of limitation, thecontextual information may comprise information about whether user 102is sitting or standing. In another example, the contextual informationmay comprise information about an application the user is interactingwith when the upper body pose is determined, for example and not by wayof limitation, an artificial reality application (e.g., whether the useris interacting with application associated with a business meeting, orif they are interacting with an application associated with a game, suchas a dance contest). In particular embodiments, the Generator 405 mayreceive one or more contextual information associated with theartificial reality system 100. In particular embodiments, the lower bodypose 215 is generated by further processing the contextual informationusing the machine-learning model.

In particular embodiments, the computing system may determine one ormore physical constraints associated with an upper body pose or lowerbody pose, using for example, a physics aware data augmentation method.The Generator 405 may receive and utilize one or more of these physicalconstraints to generate more realistic lower body poses, or a morerealistic temporal sequence of lower body poses. These physicalconstraints may be associated with one or more joint poses of the user,for example and not by way of limitation, a physical limitation on thepossible range of motion for a knee joint pose that simulates one ormore physical limitations of the human body. In particular embodiments,the computing system may determine one or more physical constraints whengenerating a temporal sequence of lower body poses. For example and notby way of limitation, the computing system may determine temporallimitations on the rate of acceleration or jitter of one or more jointsbetween one or more sequential poses in a temporal sequence of poses. Inparticular embodiments, the Generator 405 may use this constraint todetermine a more realistic temporal sequence of lower body poses.

At step 530, the system may generate, based on the upper body pose 205and the lower body pose 215, a full body pose 425. FIG. 6 illustratesgenerating a full body pose from a generated upper body pose and agenerated lower body pose. In particular embodiments, the upper bodypose 215 is generated using the methods described herein and correspondsto a first portion of a body of a user, the first portion of the bodycomprising a head and arm of a user. In particular embodiments, thelower body pose 215 is generated using the methods described herein andcorresponds to a second portion of the body of a user, the secondportion of a body comprising a leg of the user. In particularembodiments, full body pose 425 may generated by the machine-learningmodel 400. In particular embodiments, full body pose 425 may begenerated by the computing system in a post-processing step.

At step 540, the system may determine, using the Discriminator 410,whether the full body pose 425 is likely generated using the lower bodypose 215 generated by the Generator 405 (i.e., whether it is “fake” or“real”). In particular embodiments, the goal of training theDiscriminator is to have it learn how to correctly discriminate between“real” and “fake” full body poses. For example, for each full body pose,the Discriminator 410 may output a value between 0 and 1 that representsa confidence or probability/likelihood of the pose being generated bythe Generator (i.e., “fake”). For instance, a value closer to 1 mayindicate a higher probability/confidence that the pose is “real” (i.e.,not generated by the Generator) and a value closer to 0 may indicate alower probability/confidence that the pose is “real” (which implicitlymeans a higher probability/confidence that the pose is “fake”). Thesepredictions may be compared to known labels of the poses that indicatewhich are “fake” and which are “real.”

In particular embodiments, the system may compute a loss based on D'sdetermination of whether the full body pose is likely generated usingthe lower body pose 215 generated by the Generator 405. For instance,the loss may be computed based on a comparison of the prediction (e.g.,confidence/probability score) made by the Discriminator and the knownlabel (which may be an implicit label) of the full body pose being“fake.” In particular embodiments, for a given pose, the prediction ofthe Discriminator 410 (represented as an output a value between 0 and 1that represents a confidence or probability/likelihood of the pose beinggenerated by the Generator 405) may be compared to known labels of theposes that indicate which are “fake” and which are “real.” For example,if a full body pose is known to be “fake,” a prediction that is closerto 1 may result in a higher loss and a prediction that is closer to 0may result in a lower loss. The loss, in other words, may be a measureof the correctness of the Discriminator's 410 predictions.

At step 550, the system may then update the Generator based on the loss.The parameters of the Generator may be updated with the goal ofmaximizing the loss outputted by Discriminator 410. Since theGenerator's objective is to “fool” the Discriminator in thinking thatthe full body poses generated using the lower body pose 215 generated bythe Generator are “real,” the training algorithm may be configured tooptimize the loss. In other words, the Generator's parameters would beupdated with the goal optimizing the Generator to generate lower bodyposes (that are then utilized to generate a full body pose) that wouldcause the loss of the Discriminator's prediction to increase (i.e., toincrease its incorrect predictions that a given full body pose,generated using the lower body pose 215 generated by the Generator 405,is unlikely generated using the Generator 405).

Then at step 560, the system may determine whether the training ofGenerator 405 is complete. In particular embodiments the system maydetermine whether the training is complete based on one or moretermination criteria. For example, if the loss is below a predeterminedthreshold and/or if its changes over the last several iterations havestabilized (e.g., fluctuated within a predetermined range), then thesystem may determine that training is complete. Alternatively oradditionally, training may be deemed complete if a predetermined numberof training iterations have been completed or if a predetermined numberof training samples have been used. In the event training is not yetcomplete, the system may, in particular embodiments, repeat the processstarting from step 510 to continue training the Generator. If it isinstead determined that training is complete, then the Trained Generatormay be used in operation.

The training objective is for the Generator 405 to learn to generateposes 215 that would fool the Discriminator 410 in thinking the posesare “real.” In particular embodiments, the Generator 405 may generate alower body pose 215 and the Discriminator 410 may predict a likelihoodof the resulting full body pose 425 being “real” or “fake” 450. If ahigh predicted value represents the Discriminator 410 thinking that thepose is more likely to be “real,” then the objective of the training maybe expressed in terms of maximizing the Discriminator's 410 predictedvalue for the “fake” full body pose 215. If the Discriminator's 410prediction correctness for the “fake” full body pose 215 is representedas a loss 460, then the objective of the training may be expressed interms of maximizing that loss 460. Based on the loss function andtraining objective, the parameters of the Generator 405 may beiteratively updated after each training so that the Generator 405becomes better at generating lower body poses 215 that could “fool” theDiscriminator 410. Thus, once trained, the Generator 405 may be used toprocess a given upper body pose and automatically generate a realisticlower body pose.

Returning to FIG. 4 , while the Generator 405 is being trained, it maybe used to simultaneously train the Discriminator 410 in accordance withparticular embodiments. At a high level, the Generator 405 may process agiven upper body pose 205 and generate a lower body pose 215. Thecomputing system may utilize the upper body pose 205 and the lower bodypose 215 to generate a full body pose 425. The generated full body pose425, along with one or more training full body poses 435 from a trainingpose database 440 may be provided as input to the Discriminator 410.

FIG. 7 illustrates an example method 700 for training a Discriminator410. In particular embodiments, the Generator 405 may be used to trainthe Discriminator 410. In particular embodiments, the Generator 405 andthe Discriminator 410 may be trained simultaneously. The Discriminator410 may be tasked with processing the input poses (e.g., the “fake” fullbody pose 425, and the “real” training full body pose 435) andpredicting which are “real” and which are “fake” 450. Conceptually, thegoal of training the Discriminator 410 may be to maximize theDiscriminator's 410 prediction values for the generated full body pose425 and the training full body pose 435, and minimize theDiscriminator's 410 prediction value for the generated full body pose425. In other words, if the Discriminator's 410 prediction correctnessfor the “fake” pose (e.g., the generated full body pose 425) and the“real” poses (i.e., the training full body pose 435) are represented aslosses 460 and 465, respectively, the goal of training the Discriminator410 would be to minimize the losses (i.e., minimize the incorrectpredictions). Based on the loss function and training objective, theparameters of the Discriminator 410 may be iteratively updated aftereach prediction so that the Discriminator 410 becomes better atdiscriminating between “real” and “fake” full body poses.

The method may begin at step 710, where a computing system may receivean upper body pose 205 that corresponds to a first portion of a body ofuser 102, the first portion of the body comprising a head and arm ofuser 102. The upper body pose 205 inputted into the Generator 405 may bean upper body pose generated based on sensor data and/or image datausing one or more techniques described herein, or the upper body pose205 inputted into the Generator 405 may be an upper body pose extractedfrom one or more real images of a person. In particular embodiments step710 may proceed similarly to step 510 described herein.

At step 720, the machine-learning model may generate, by processing theupper body pose 205, a lower body pose 215 that corresponds to a secondportion of the body of user 102, the second portion of the bodycomprising a leg of user 102. In particular embodiments the Generator405 may generate the lower body pose 215 based on the received upperbody pose 205. In particular embodiments the generated lower body pose215 corresponds to a second portion of the body comprising a leg of theuser. In particular embodiments, the Generator 405 may generate thelower body pose 215 based on its current parameters, which may beiteratively updated during training so that the Generator gets better atgenerating realistic lower body poses. The generated lower body pose 215may be used to generate a “fake” full body pose 425 that comprises thegenerated lower body pose 215. In particular embodiments step 720 mayproceed similarly to step 520 described herein.

In particular embodiments the computing system may determine contextualinformation associated with the artificial reality system 100. Inparticular embodiments, the Generator may receive the contextualinformation. The Generator 405 may utilize this contextual informationfor generating lower body poses. This contextual information may beassociated with a particular time at which the sensor data and/or imagedata was captured. For example and not by way of limitation, thecontextual information may comprise information about whether user 102is sitting or standing. In another example, the contextual informationmay comprise information about an application the user is interactingwith when the upper body pose is determined, for example and not by wayof limitation, an artificial reality application (e.g., whether the useris interacting with application associated with a business meeting, orif they are interacting with an application associated with a game, suchas a dance contest). In particular embodiments, the Generator 405 mayreceive one or more contextual information associated with theartificial reality system 100. In particular embodiments, the lower bodypose 215 is generated by further processing the contextual informationusing the machine-learning model.

At step 730, the system may generate, based on the upper body pose 205and the lower body pose 215, a full body pose 425. In particularembodiments, full body pose 425 may generated by the machine-learningmodel 400. In particular embodiments, full body pose 425 may begenerated by the computing system in a post-processing step. Inparticular embodiments step 730 may proceed similarly to step 530described herein.

At step 740, the system may determine, using the Discriminator 410,whether the full body pose 425 is likely generated using the lower bodypose 215 generated by the generated using the Generator 405 (i.e.,whether it is “fake” or “real”). In particular embodiments, the goal oftraining the Discriminator is to have it learn how to correctlydiscriminate between “real” and “fake” full body poses. For example, foreach full body pose, the Discriminator 410 may output a value between 0and 1 that represents a confidence or probability/likelihood of the posebeing generated by the Generator (i.e., “fake”). For instance, a valuecloser to 1 may indicate a higher probability/confidence that the poseis “real” (i.e., not generated by the Generator) and a value closer to 0may indicate a lower probability/confidence that the pose is “real”(which implicitly means a higher probability/confidence that the pose is“fake”). These predictions may be compared to known labels of the posesthat indicate which are “fake” and which are “real.” In particularembodiments step 740 may proceed similarly to step 540 described herein.

In particular embodiments, the system may compute a loss based on theDiscriminator's determination of whether the full body pose is likelygenerated using the lower body pose 215 generated by the generated bythe Generator 405. For instance, the loss may be computed based on acomparison of the prediction (e.g., confidence/probability score) madeby the Discriminator and the known label (which may be an implicitlabel) of the full body pose being “fake.” In particular embodiments,for a given pose, the prediction of the Discriminator 410 (representedas an output a value between 0 and 1 that represents a confidence orprobability/likelihood of the pose being generated by the Generator 405)may be compared to known labels of the poses that indicate which are“fake” and which are “real.” For example, if a full body pose is knownto be “fake,” a prediction that is closer to 1 may result in a higherloss and a prediction that is closer to 0 may result in a lower loss.The loss, in other words, may be a measure of the correctness of theDiscriminator's 410 predictions.

At step 750, the system may then update the Discriminator based on theloss. The parameters of the Discriminator 410 may be updated with thegoal of minimizing the loss outputted by the Discriminator. Inparticular embodiments, these losses may be back-propagated and used bythe training algorithm to update the parameters of the Discriminator 410so that, over the course of the training, the Discriminator wouldprogressively become better at discriminating between “fake” versus“real” poses. The goal of the training algorithm may be to minimize thelosses (i.e., minimize the incorrect predictions).

Then at step 760, the system may determine whether the training ofDiscriminator 405 is complete. In particular embodiments the system maydetermine whether the training is complete based on one or moretermination criteria. For example, if the loss is below a predeterminedthreshold and/or if its changes over the last several iterations havestabilized (e.g., fluctuated within a predetermined range), then thesystem may determine that training is complete. Alternatively oradditionally, training may be deemed complete if a predetermined numberof training iterations have been completed or if a predetermined numberof training samples have been used. In the event training is not yetcomplete, the system may, in particular embodiments, repeat the processstarting from step 710 to continue training the Discriminator.

Although this disclosure describes and illustrates particular steps ofthe method of FIG. 7 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 7 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for training a Discriminator, includingthe particular steps of the method of FIG. 7 , this disclosurecontemplates any suitable method for training a Discriminator, includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 7 , where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 7 , thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 7 .

Returning to FIG. 5 , once the Generator 405 has been trained, it can beconfigured to receive an input upper body pose 205 and generate a lowerbody pose 215. After training, the pose would be realistic, such thatwhen combined with the upper body pose 205 the computing system wouldgenerate a realistic full body pose 425. Further, once the Generator istrained, it may be used to generate lower body poses based on anyinputted upper body pose (in other words, it is not limited togenerating lower body poses based on poses that appeared in the trainingpose dataset). The trained Generator may also be distributed todifferent platforms different from the training system, including, forexample, users' mobile devices or other personal computing devices.

At step 570 the Trained Generator may access an upper body pose 205. Theupper body pose 205 may correspond to a first portion of a body of user102, the first portion of the body comprising a head and arm of user102. The upper body pose 205 inputted into the Trained Generator may bean upper body pose generated based on sensor data using one or moretechniques described herein, or the upper body pose 205 inputted intothe Trained Generator 405 may be an upper body pose from one or morereal images of a person.

Then at step 580, the Trainer Generator may generate a lower body posethat corresponds to a second portion of the body of the user. The lowerbody pose may correspond to a second portion of the body of user 102,the second portion of the body comprising a leg of user 102. Inparticular embodiments the Trained Generator may generate the lower bodypose 215 based on the received upper body pose 205. In particularembodiments the generated lower body pose 215 corresponds to a secondportion of the body comprising a leg of the user.

In particular embodiments, the trained Generator may also receive andprocess contextual information specifying further information forgenerating an accurate lower body pose 215. In particular embodimentsthe contextual information may be associated with a time at which thesensor data is captured that was used to generate upper body pose 205.For example, the Generator may receive contextual information comprisingan application the user 102 was interacting with when sensor data wascaptured, such as a virtual reality application used for virtualbusiness meetings. Based on this information, the trained Generator 405may be more likely to generate a lower body pose 215 that is typical ofa user sitting or standing in a workplace setting.

Returning to FIG. 6 , In particular embodiments the computing system maygenerate based on the upper body pose 205 and the lower body pose 215, afull body pose 425. In particular embodiments, full body pose 425 may begenerated by the machine-learning model 400. In particular embodiments,full body pose 425 may be generated by the computing system in apost-processing step. In particular embodiments, this full body pose 425may be utilized for a variety of applications. For example, computingsystem 108 may utilize fully body pose 425 to generate an avatar of user102 in a virtual reality or artificial reality space. In particularembodiments the computing system 108 may only utilize a portion of thefull body pose 425 for various applications, for example and not by wayof limitation, an upper body pose (e.g., from the user's head to theuser's hip) or the inferred pose of only one or more joints (e.g. anelbow joint 230, or a knee joint 290).

Although this disclosure describes and illustrates particular steps ofthe method of FIG. 5 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 5 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for training a generator based on encodedrepresentations of features, including the particular steps of themethod of FIG. 5 , this disclosure contemplates any suitable method fortraining a generator, including any suitable steps, which may includeall, some, or none of the steps of the method of FIG. 5 , whereappropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 5 , this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 5 .

Although this disclosure describes and illustrates particular steps ofthe method of FIG. 4 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 4 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for training a generator based onrecurrence loss, including the particular steps of the method of FIG. 4, this disclosure contemplates any suitable method for for training agenerator based on recurrence loss, including any suitable steps, whichmay include all, some, or none of the steps of the method of FIG. 4 ,where appropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 4 , this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 4 .

FIG. 8 illustrates an example method 800 for generating a full body poseof the user based on an upper body pose and a lower body pose. Themethod may begin at step 810, where a computing system may receivesensor data captured by one more sensors coupled to a user. Inparticular embodiments the one or more sensors may be coupled to user102. In particular embodiments, the one or more sensors may beassociated with the headset 104 worn by the user. For example and not byway of limitation, the headset 104 may include a gyroscope or inertialmeasurement unit that tracks the user's real-time movements and outputsensor data to represent or describe the movement. As another exampleand not by way of limitation, the one or more controllers 106 mayinclude inertial measurement units (IMUs) and infrared (IR) lightemitting diodes (LEDs) configured to collect and send IMU sensor data tothe computing system 108. As another example, the sensor data maycomprise one or more image data from one or more components ofartificial reality system 100.

At step 820, the computing system may generate an upper body pose thatcorresponds to a first portion of a body of the user, the first portionof the body comprising a head and arm of the user. In particularembodiments the computing system may utilize sensor data to generate theupper body pose. In particular embodiments, computing system 108 maydetermine a plurality of poses corresponding to a plurality ofpredetermined body parts of user 102, for example and not by way oflimitation, a joint pose (e.g., a head pose 210 or a wrist pose 220). Inparticular embodiments one or more joint poses comprising the upper bodypose 200 may be determined or inferred using a combination of one ormore techniques, for example and not by way of limitation localizationtechniques (e.g., SLAM), machine-learning techniques (e.g., a neuralnetwork), known spatial relationships with one or more artificialreality system components (e.g., a known spatial relationship betweenheadset 104 and head pose 210), visualization techniques (e.g., imagesegmentation), or optimization techniques (e.g., a non-linear solver).

At step 830, the computing system may generate a lower body pose thatcorresponds to a second portion of the body of the user, the secondportion of the body comprising a leg of the user. In particularembodiments, the computing system may utilize a machine-learning modelto generate the lower body pose, for example and not by way oflimitation, a Generative Adversarial Network (GAN) comprising aGenerator network and a Discriminator Network. In particularembodiments, the Generator may receive contextual information associatedwith the upper body pose. The Generator may utilize this contextualinformation for generating lower body poses. This contextual informationmay be associated with a particular time at which the sensor data and/orimage data was captured.

At step 840, the computing system may generate a full body pose of theuser based on the upper body pose and the lower body pose. In particularembodiments, full body pose may be generated by the machine-learningmodel. In particular embodiments, full body pose may be generated by thecomputing system in a post-processing step. In particular embodiments,this full body pose may be utilized for a variety of applications. Forexample, computing system may utilize fully body pose to generate anavatar of user in a virtual reality or artificial reality space.

Particular embodiments may repeat one or more steps of the method ofFIG. 8 , where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 8 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 8 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method forgenerating a full body pose of the user based on an upper body pose anda lower body pose, including the particular steps of the method of FIG.8 , this disclosure contemplates any suitable method for generating afull body pose of the user based on an upper body pose and a lower bodypose, including any suitable steps, which may include all, some, or noneof the steps of the method of FIG. 8 , where appropriate. Furthermore,although this disclosure describes and illustrates particularcomponents, devices, or systems carrying out particular steps of themethod of FIG. 8 , this disclosure contemplates any suitable combinationof any suitable components, devices, or systems carrying out anysuitable steps of the method of FIG. 8 .

FIG. 9 illustrates an example network environment 900 associated with asocial-networking system. Network environment 900 includes a clientsystem 930, a social-networking system 960, and a third-party system 970connected to each other by a network 910. Although FIG. 9 illustrates aparticular arrangement of client system 930, social-networking system960, third-party system 970, and network 910, this disclosurecontemplates any suitable arrangement of client system 930,social-networking system 960, third-party system 970, and network 910.As an example and not by way of limitation, two or more of client system930, social-networking system 960, and third-party system 970 may beconnected to each other directly, bypassing network 910. As anotherexample, two or more of client system 930, social-networking system 960,and third-party system 970 may be physically or logically co-locatedwith each other in whole or in part. Moreover, although FIG. 9illustrates a particular number of client systems 930, social-networkingsystems 960, third-party systems 970, and networks 910, this disclosurecontemplates any suitable number of client systems 930,social-networking systems 960, third-party systems 970, and networks910. As an example and not by way of limitation, network environment 900may include multiple client system 930, social-networking systems 960,third-party systems 970, and networks 910.

This disclosure contemplates any suitable network 910. As an example andnot by way of limitation, one or more portions of network 910 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. Network 910 may include one or more networks910.

Links 950 may connect client system 930, social-networking system 960,and third-party system 970 to communication network 910 or to eachother. This disclosure contemplates any suitable links 950. Inparticular embodiments, one or more links 950 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOCSIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 950 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 950, or a combination of two or more such links950. Links 950 need not necessarily be the same throughout networkenvironment 900. One or more first links 950 may differ in one or morerespects from one or more second links 950.

In particular embodiments, client system 930 may be an electronic deviceincluding hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by clientsystem 930. As an example and not by way of limitation, a client system930 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, augmented/virtual realitydevice, other suitable electronic device, or any suitable combinationthereof. This disclosure contemplates any suitable client systems 930. Aclient system 930 may enable a network user at client system 930 toaccess network 910. A client system 930 may enable its user tocommunicate with other users at other client systems 930.

In particular embodiments, client system 930 may include a web browser932, and may have one or more add-ons, plug-ins, or other extensions. Auser at client system 930 may enter a Uniform Resource Locator (URL) orother address directing the web browser 932 to a particular server (suchas server 962, or a server associated with a third-party system 970),and the web browser 932 may generate a Hyper Text Transfer Protocol(HTTP) request and communicate the HTTP request to server. The servermay accept the HTTP request and communicate to client system 930 one ormore Hyper Text Markup Language (HTML) files responsive to the HTTPrequest. Client system 930 may render a webpage based on the HTML filesfrom the server for presentation to the user. This disclosurecontemplates any suitable webpage files. As an example and not by way oflimitation, webpages may render from HTML files, Extensible Hyper TextMarkup Language (XHTML) files, or Extensible Markup Language (XML)files, according to particular needs. Such pages may also executescripts, combinations of markup language and scripts, and the like.Herein, reference to a webpage encompasses one or more correspondingwebpage files (which a browser may use to render the webpage) and viceversa, where appropriate.

In particular embodiments, social-networking system 960 may be anetwork-addressable computing system that can host an online socialnetwork. Social-networking system 960 may generate, store, receive, andsend social-networking data, such as, for example, user-profile data,concept-profile data, social-graph information, or other suitable datarelated to the online social network. Social-networking system 960 maybe accessed by the other components of network environment 900 eitherdirectly or via network 910. As an example and not by way of limitation,client system 930 may access social-networking system 960 using a webbrowser 932, or a native application associated with social-networkingsystem 960 (e.g., a mobile social-networking application, a messagingapplication, another suitable application, or any combination thereof)either directly or via network 910. In particular embodiments,social-networking system 960 may include one or more servers 962. Eachserver 962 may be a unitary server or a distributed server spanningmultiple computers or multiple datacenters. Servers 962 may be ofvarious types, such as, for example and without limitation, web server,news server, mail server, message server, advertising server, fileserver, application server, exchange server, database server, proxyserver, another server suitable for performing functions or processesdescribed herein, or any combination thereof. In particular embodiments,each server 962 may include hardware, software, or embedded logiccomponents or a combination of two or more such components for carryingout the appropriate functionalities implemented or supported by server962. In particular embodiments, social-networking system 960 may includeone or more data stores 964. Data stores 964 may be used to storevarious types of information. In particular embodiments, the informationstored in data stores 964 may be organized according to specific datastructures. In particular embodiments, each data store 964 may be arelational, columnar, correlation, or other suitable database. Althoughthis disclosure describes or illustrates particular types of databases,this disclosure contemplates any suitable types of databases. Particularembodiments may provide interfaces that enable a client system 930, asocial-networking system 960, or a third-party system 970 to manage,retrieve, modify, add, or delete, the information stored in data store964.

In particular embodiments, social-networking system 960 may store one ormore social graphs in one or more data stores 964. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. Social-networking system 960 mayprovide users of the online social network the ability to communicateand interact with other users. In particular embodiments, users may jointhe online social network via social-networking system 960 and then addconnections (e.g., relationships) to a number of other users ofsocial-networking system 960 to whom they want to be connected. Herein,the term “friend” may refer to any other user of social-networkingsystem 960 with whom a user has formed a connection, association, orrelationship via social-networking system 960.

In particular embodiments, social-networking system 960 may provideusers with the ability to take actions on various types of items orobjects, supported by social-networking system 960. As an example andnot by way of limitation, the items and objects may include groups orsocial networks to which users of social-networking system 960 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use, transactions that allowusers to buy or sell items via the service, interactions withadvertisements that a user may perform, or other suitable items orobjects. A user may interact with anything that is capable of beingrepresented in social-networking system 960 or by an external system ofthird-party system 970, which is separate from social-networking system960 and coupled to social-networking system 960 via a network 910.

In particular embodiments, social-networking system 960 may be capableof linking a variety of entities. As an example and not by way oflimitation, social-networking system 960 may enable users to interactwith each other as well as receive content from third-party systems 970or other entities, or to allow users to interact with these entitiesthrough an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 970 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 970 may beoperated by a different entity from an entity operatingsocial-networking system 960. In particular embodiments, however,social-networking system 960 and third-party systems 970 may operate inconjunction with each other to provide social-networking services tousers of social-networking system 960 or third-party systems 970. Inthis sense, social-networking system 960 may provide a platform, orbackbone, which other systems, such as third-party systems 970, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 970 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 930. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, social-networking system 960 also includesuser-generated content objects, which may enhance a user's interactionswith social-networking system 960. User-generated content may includeanything a user can add, upload, send, or “post” to social-networkingsystem 960. As an example and not by way of limitation, a usercommunicates posts to social-networking system 960 from a client system930. Posts may include data such as status updates or other textualdata, location information, photos, videos, links, music or othersimilar data or media. Content may also be added to social-networkingsystem 960 by a third-party through a “communication channel,” such as anewsfeed or stream.

In particular embodiments, social-networking system 960 may include avariety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, social-networking system 960 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. Social-networking system960 may also include suitable components such as network interfaces,security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments,social-networking system 960 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking social-networking system 960 to one or more client systems 930or one or more third-party system 970 via network 910. The web servermay include a mail server or other messaging functionality for receivingand routing messages between social-networking system 960 and one ormore client systems 930. An API-request server may allow a third-partysystem 970 to access information from social-networking system 960 bycalling one or more APIs. An action logger may be used to receivecommunications from a web server about a user's actions on or offsocial-networking system 960. In conjunction with the action log, athird-party-content-object log may be maintained of user exposures tothird-party-content objects. A notification controller may provideinformation regarding content objects to a client system 930.Information may be pushed to a client system 930 as notifications, orinformation may be pulled from client system 930 responsive to a requestreceived from client system 930. Authorization servers may be used toenforce one or more privacy settings of the users of social-networkingsystem 960. A privacy setting of a user determines how particularinformation associated with a user can be shared. The authorizationserver may allow users to opt in to or opt out of having their actionslogged by social-networking system 960 or shared with other systems(e.g., third-party system 970), such as, for example, by settingappropriate privacy settings. Third-party-content-object stores may beused to store content objects received from third parties, such as athird-party system 970. Location stores may be used for storing locationinformation received from client systems 930 associated with users.Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

FIG. 10 illustrates an example computer system 1000. In particularembodiments, one or more computer systems 1000 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1000 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1000 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1000.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1000. This disclosure contemplates computer system 1000 taking anysuitable physical form. As example and not by way of limitation,computer system 1000 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, an augmented/virtual reality device, or a combinationof two or more of these. Where appropriate, computer system 1000 mayinclude one or more computer systems 1000; be unitary or distributed;span multiple locations; span multiple machines; span multiple datacenters; or reside in a cloud, which may include one or more cloudcomponents in one or more networks. Where appropriate, one or morecomputer systems 1000 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 1000 may perform in real time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 1000 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 1000 includes a processor1002, memory 1004, storage 1006, an input/output (I/O) interface 1008, acommunication interface 1010, and a bus 1012. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1002 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1002 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1004, or storage 1006; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1004, or storage 1006. In particularembodiments, processor 1002 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1002 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1002 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1004 or storage 1006, and the instruction caches may speed upretrieval of those instructions by processor 1002. Data in the datacaches may be copies of data in memory 1004 or storage 1006 forinstructions executing at processor 1002 to operate on; the results ofprevious instructions executed at processor 1002 for access bysubsequent instructions executing at processor 1002 or for writing tomemory 1004 or storage 1006; or other suitable data. The data caches mayspeed up read or write operations by processor 1002. The TLBs may speedup virtual-address translation for processor 1002. In particularembodiments, processor 1002 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1002 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1002 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1002. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1004 includes main memory for storinginstructions for processor 1002 to execute or data for processor 1002 tooperate on. As an example and not by way of limitation, computer system1000 may load instructions from storage 1006 or another source (such as,for example, another computer system 1000) to memory 1004. Processor1002 may then load the instructions from memory 1004 to an internalregister or internal cache. To execute the instructions, processor 1002may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1002 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1002 may then write one or more of those results to memory 1004. Inparticular embodiments, processor 1002 executes only instructions in oneor more internal registers or internal caches or in memory 1004 (asopposed to storage 1006 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1004 (asopposed to storage 1006 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1002 to memory 1004. Bus 1012 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1002 and memory 1004and facilitate accesses to memory 1004 requested by processor 1002. Inparticular embodiments, memory 1004 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1004 may include one ormore memories 1004, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1006 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1006 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1006 may include removable or non-removable (or fixed)media, where appropriate. Storage 1006 may be internal or external tocomputer system 1000, where appropriate. In particular embodiments,storage 1006 is non-volatile, solid-state memory. In particularembodiments, storage 1006 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1006taking any suitable physical form. Storage 1006 may include one or morestorage control units facilitating communication between processor 1002and storage 1006, where appropriate. Where appropriate, storage 1006 mayinclude one or more storages 1006. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1008 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1000 and one or more I/O devices. Computersystem 1000 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1000. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1008 for them. Where appropriate, I/Ointerface 1008 may include one or more device or software driversenabling processor 1002 to drive one or more of these I/O devices. I/Ointerface 1008 may include one or more I/O interfaces 1008, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1010 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1000 and one or more other computer systems 1000 or oneor more networks. As an example and not by way of limitation,communication interface 1010 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1010 for it. As an example and not by way oflimitation, computer system 1000 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1000 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1000 may include any suitable communicationinterface 1010 for any of these networks, where appropriate.Communication interface 1010 may include one or more communicationinterfaces 1010, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1012 includes hardware, software, or bothcoupling components of computer system 1000 to each other. As an exampleand not by way of limitation, bus 1012 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1012may include one or more buses 1012, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by a computing system:receiving sensor data captured at a first time by one or more sensorscoupled to a user; generating, based on the sensor data, a first upperbody pose associated with the first time, wherein the first upper bodypose corresponds to a first portion of a body of the user, the firstportion of the body comprising a head and an arm of the user; generatinga temporal sequence of lower body poses comprising a first lower bodypose associated with the first time and a second lower body poseassociated with a second time by processing the first upper body poseusing a machine-learning model, wherein the temporal sequence of lowerbody poses correspond to a second portion of the body of the user, thesecond portion of the body comprising a leg of the user; generating oneor more full body poses of the user based on at least the first upperbody pose and the temporal sequence of lower body poses.
 2. The methodof claim 1, wherein the machine-learning model is trained using a secondmachine-learning model trained to determine whether a given full bodypose is likely generated using the machine-learning model.
 3. The methodof claim 1, wherein the machine-learning model is optimized duringtraining to cause a second machine-learning model to incorrectlydetermine that a given full body pose, generated using themachine-learning model, is unlikely generated using the machine-learningmodel.
 4. The method of claim 1, wherein the one or more sensors areassociated with a head-mounted device worn by the user.
 5. The method ofclaim 1, wherein generating the first upper body pose comprises:determining, based on the sensor data, a plurality of posescorresponding to a plurality of predetermined body parts of the user;and inferring the first upper body pose based on the plurality of poses.6. The method of claim 5, wherein the first upper body pose furthercomprises a wrist pose corresponding to a wrist of the user, wherein thewrist pose is determined based on one or more images captured by ahead-mounted device worn by the user, the one or more images depicting(1) the wrist of the user or (2) a device held by the user.
 7. Themethod of claim 1, further comprising determining contextual informationassociated with the first time, wherein the first and second lower bodyposes are generated by further processing the contextual informationusing the machine-learning model.
 8. The method of claim 7, wherein thecontextual information comprises an application the user is interactingwith when the first upper body pose is determined.
 9. The method ofclaim 7, wherein the contextual information indicates the user isstanding or sitting at the first time.
 10. The method of claim 1,further comprising generating, based on the one or more full body poses,an avatar of the user.
 11. The method of claim 1, wherein the first andsecond lower body poses are generated by further processing one or morephysical constraints associated with the first upper body pose using themachine-learning model.
 12. The method of claim 11, wherein the one ormore physical constraints comprise a physical limitation on a range ofmotion of one or more joints of the user.
 13. One or morecomputer-readable non-transitory storage media comprising software thatis operable when executed by a server to: receive sensor data capturedat a first time by one or more sensors coupled to a user; generate,based on the sensor data, a first upper body pose associated with thefirst time, wherein the first upper body pose corresponds to a firstportion of a body of the user, the first portion of the body comprisinga head and an arm of the user; generate a temporal sequence of lowerbody poses comprising a first lower body pose associated with the firsttime and a second lower body pose associated with a second time byprocessing the first upper body pose using a machine-learning model,wherein the temporal sequence of lower body poses correspond to a secondportion of the body of the user, the second portion of the bodycomprising a leg of the user; generate one or more full body poses ofthe user based on at least the first upper body pose and the temporalsequence of lower body poses.
 14. The media of claim 13, whereingenerating the first upper body pose comprises: determining, based onthe sensor data, a plurality of poses corresponding to a plurality ofpredetermined body parts of the user; and inferring the first upper bodypose based on the plurality of poses.
 15. The media of claim 13, whereinthe software is further operable when executed to determine contextualinformation associated with the first time, wherein the first and secondlower body poses are generated by further processing the contextualinformation using the machine-learning model.
 16. The media of claim 15,wherein the contextual information comprises an application the user isinteracting with when the first upper body pose is determined.
 17. Themedia of claim 13, wherein the software is further operable whenexecuted to generate, based on the one or more full body poses, anavatar of the user.
 18. The media of claim 13, wherein the first andsecond lower body poses are generated by further processing one or morephysical constraints associated with the first upper body pose using themachine-learning model.
 19. A system comprising: one or more processors;and one or more computer-readable non-transitory storage media coupledto one or more of the processors and comprising instructions operablewhen executed by one or more of the processors to cause the system to:receive sensor data captured at a first time by one or more sensorscoupled to a user; generate, based on the sensor data, a first upperbody pose associated with the first time, wherein the first upper bodypose corresponds to a first portion of a body of the user, the firstportion of the body comprising a head and an arm of the user; generate atemporal sequence of lower body poses comprising a first lower body poseassociated with the first time and a second lower body pose associatedwith a second time by processing the first upper body pose using amachine-learning model, wherein the temporal sequence of lower bodyposes correspond to a second portion of the body of the user, the secondportion of the body comprising a leg of the user; generate one or morefull body poses of the user based on at least the first upper body poseand the temporal sequence of lower body poses.
 20. The system of claim19, wherein generating the first upper body pose comprises: determining,based on the sensor data, a plurality of poses corresponding to aplurality of predetermined body parts of the user; and inferring thefirst upper body pose based on the plurality of poses.